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This Notebook kernel deals with a dataset from 2017 of three 3 MW windturbines. It covers an EDA and Data Visualization of the Turbine and and the comparison of different regression Models including Decision Tree, Random Forrest, Support Vector Machine and ANN.

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ChristinaRippl/wind_turbine_yield_regression

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wind_turbine_yield_regression

Introduction

This Notebook kernel deals with a dataset from 2017 of three 3 MW windturbines. It covers an EDA and Data Visualization of the Turbine and and the comparison of different regression Models including Decission Tree, Random Forrest, Support Vector Machine and ANN.

Business case

Due to climate change, the demand for renewable energy is increasing. Here, wind turbines offer a clean approach to contribute to climate protection. However, wind speeds and thus the energy yield change rapidly. An approach of the models on the basis of classical weather forecasts to predict the future energy supply. This is of interest for the wind turbine operators as well as for the power grid operators.

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This Notebook kernel deals with a dataset from 2017 of three 3 MW windturbines. It covers an EDA and Data Visualization of the Turbine and and the comparison of different regression Models including Decision Tree, Random Forrest, Support Vector Machine and ANN.

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